class_docstr: Multi-headed attention.
  
  See "Attention Is All You Need" for more details.
code: def _relative_positions_bucket(
      self, relative_positions, bidirectional=True
  ):
      num_buckets = self.num_buckets
      max_distance = self.max_distance
      relative_buckets = 0
  
      if bidirectional:
          num_buckets = num_buckets // 2
          relative_buckets += (relative_positions > 0).to(
              torch.long
          ) * num_buckets
          relative_positions = torch.abs(relative_positions)
      else:
          relative_positions = -torch.min(
              relative_positions, torch.zeros_like(relative_positions)
          )
  
      max_exact = num_buckets // 2
      is_small = relative_positions < max_exact
  
      relative_postion_if_large = max_exact + (
          torch.log(relative_positions.float() / max_exact)
          / math.log(max_distance / max_exact)
          * (num_buckets - max_exact)
      ).to(torch.long)
      relative_postion_if_large = torch.min(
          relative_postion_if_large,
          torch.full_like(relative_postion_if_large, num_buckets - 1),
      )
  
      relative_buckets += torch.where(
          is_small, relative_positions, relative_postion_if_large
      )
      return relative_buckets
  
imports: import math
  import numpy
  from typing import Dict, Optional, Tuple
  import torch
  from torch import Tensor, nn
  import torch.nn.functional
  from torch.nn import LayerNorm, Parameter
  from lavis.models.beats.modules import GradMultiply, SamePad, get_activation_fn, GLU_Linear, quant_noise
label_desc: Use image node_id 3 for calling the MultiheadAttention obj's underlying member method code with example usage: obj._relative_positions_bucket(relative_positions, bidirectional) and returns: relative_buckets